Articulation differences of /s/ observed using inverse tongue atlas modeling
Bibliographic record
Abstract
Depending on the vowel context, the consonant /s/ can be articulated by engaging different functional units of the tongue. An inverse finite element (FE) tongue model comprising hexahedral elements generated from a 4D statistical MRI atlas of 22 speakers performing the speech tasks “a geese” (tongue moving forwards) and “a souk” (tongue moving backwards) is used to study this articulatory behavior. The model uses a state-of-the-art inverse tracking controller to simulate the motion of internal tissue points of the different speakers deformed into the atlas space. Motion tracking is successfully carried out by minimizing the L2-norm of velocity error of FEM nodes using the Cottle–Dantzig algorithm. The results show that for “a-geese,” the utterance of /s/ in context of vowel /i/ showed increase in activation of tongue protruder muscles such as genioglossus posterior, floor muscle geniohyoid by ∼5% more than in the case of “a-souk.” In “a-souk” relative activity increased in the tongue retractor muscles mid- and anterior-genioglossus, and superior longitudinal by ∼4%. Our findings are consistent with subject-specific state-of-the-art models and with articulatory expectations. The inverse atlas tongue model can be further used to estimate such articulation behavior on extended datasets of subjects performing more variations of speech tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".